• Standardising the Capture and Processing of Custody Images

      Jilani, Shelina K.; Ugail, Hassan; Cole, S.; Logan, Andrew J. (2018)
      Custody images are a standard feature of everyday Policing and are commonly used during investigative work to establish whether the perpetrator and the suspect are the same. The process of identification relies heavily on the quality of a custody image because a low-quality image may mask identifying features. With an increased demand for high quality facial images and the requirement to integrate biometrics and machine vision technology to the field of face identification, this research presents an innovative image capture and biometric recording system called the Halo. Halo is a pioneering system which (1) uses machine vision cameras to capture high quality facial images from 8 planes of view (including CCTV simulated), (2) uses high quality video technology to record identification parades and, (3) records biometric data from the face by using a Convolutional Neural Networks (CNN) based algorithm, which is a supervised machine learning technique. Results based on our preliminary experiments have concluded a 100% facial recognition rate for layer 34 within the VGG-Face model. These results are significant for the sector of forensic science, especially digital image capture and facial identification as they highlight the importance of image quality and demonstrates the complementing nature a robust machine learning algorithm has on an everyday Policing process.
    • Using avatars in weight management settings: a systematic review

      Horne, M.; Hill, A.; Murells, T.; Ugail, Hassan; Irving; Chinnadorai, R.; Hardy, Maryann L. (2020-03)
      Background: Obesity interventions rely predominantly on managing dietary intake and/or increasing physical activity but sustained adherence to behavioural regimens is often poor. Avatar technology is well established within the computer gaming industry and evidence suggests that virtual representations of self may impact real-world behaviour, acting as a catalyst for sustained weight loss behaviour modification. However, the effectiveness of avatar technology in promoting weight loss is unclear. Aims: We aimed to assess the quantity and quality of empirical support for the use of avatar technologies in adult weight loss interventions. Method: A systematic review of empirical studies was undertaken. The key objectives were to determine if: (i) the inclusion of avatar technology leads to greater weight loss achievement compared to routine intervention; and (ii) whether weight loss achievement is improved by avatar personalisation (avatar visually reflects self). Results: We identified 6 papers that reported weight loss data. Avatar-based interventions for weight loss management were found to be effective in the short (4–6 weeks) and medium (3–6 months) term and improved weight loss maintenance in the long term (12 months). Only 2 papers included avatar personalisation, but results suggested there may be some added motivational benefit. Conclusions: The current evidence supports that avatars may positively impact weight loss achievement and improve motivation. However, with only 6 papers identified the evidence base is limited and therefore findings need to be interpreted with caution.